Building on data-smart to become AI-savvy

In recent years, we have seen Artificial Intelligence (AI) in the enterprise evolve from having the potential to be transformational to becoming an increasingly central component of core business processes. McKinsey found that the number of companies using AI has risen from 55% to 72% between 2023 and 2024.

To prepare for AI transformation, many businesses have doubled down on improving their data practices. However, being data-savvy is not enough to deploy AI at scale. Businesses would, therefore, be unwise to see being “data ready” as the endgame in their preparations for AI adoption. Data readiness is just one phase in the AI journey and needs to be viewed as such.

AI, like every other technology, has strengths, shortfalls and optimum operating environments. Understanding this is the difference between being data-savvy and being truly ready to realize the full potential of AI. I call this ‘AI-savviness’. Strong data management is important, but becoming AI-savvy requires organizational leaders to think bigger.

Fostering a culture that encourages continuous learning, experimentation and a fundamental understanding of when AI is and is not the solution are all steps that must be taken to capitalize on AI’s transformative potential.

Becoming data-savvy

An organization can have strong data practices and not be AI-ready. But it can’t be AI-ready without strong data practices.

The necessity to establish this foundation is made clear by Gartner’s finding that a lack of AI-ready data blocks 63% of organizations undergoing AI projects. Businesses need to ensure they have frameworks with strong governance principles and accurate and high-quality data in place before pursuing AI transformation projects.

There is a growing demand among organizations for AI applications that function in real-time and, in the near future, all applications will have a live element, especially as models become more sophisticated. Leveraging hybrid pipelines results in outputs that are more accurate due to models learning, unlearning and thinking in real-time, but businesses need to future-proof and design data frameworks with this in mind now to avoid being forced to re-platform later.

Knowing when AI isn’t the solution

The AI hype and resulting explosion in demand and solutions is far outpacing that of any other recent technological breakthrough. While this is reflective of its potential within business, it encourages an AI arms race that has left many organizations feeling they have to adopt AI at any cost or risk falling to the wayside. This pressure often leads to poorly considered, ineffective investments.

Research suggests that global enterprise AI spending is set to rise by 5.7% in 2025 despite overall IT budgets only increasing by 2%, but blindly spending large amounts of money on new AI-powered applications is not enough to prime a business’s estate for AI.

When businesses fall into this trap, they enter the “trough of disillusionment" as their high expectations go unmet. This is made worse by the chasm that currently exists in many organizations between what leaders want AI to be able to do and what it can actually do. Understanding that the most cutting-edge technology is not always the best solution is a sign of an AI-savvy business.

In a similar vein, once it is ascertained that AI is the answer, leaders must decide whether to buy or build. Building AI applications in-house gives organizations a higher level of control and more tailored outputs, but training and maintenance are complex and come with a high price tag. AI-savvy organizations fundamentally understand when custom-built capabilities are worth the extra expenditure and when more value can be extracted from buying a more generic off-the-shelf product.

Fostering a culture of experimentation

Beyond implementing an effective technology stack, leaders need to influence AI collaboration and innovation throughout their workforce, as this allows innovation and overall adoption to thrive. AI experimentation needs to be widely encouraged and reinforced as a central element of day-to-day work – and this has to come from the top.

Leaders should also practice what they preach with forward-thinking decisions that drive forward their company’s AI strategy. In the past, the fear of making incorrect decisions has guided executives. But in a business world where innovation is so closely aligned to competitive advantage, delaying AI decisions presents an even higher risk.

Employee buy-in is another essential feature of an AI-savvy business. Without it, even the most advanced and efficient AI tools become obsolete. To achieve and maintain buy-in, consistent use and ultimate return on investment, leaders need to build trust in the tools among the workforce.

The starting point for this is ensuring that the systems employees interact with consistently deliver accurate outputs. Otherwise, they will become frustrated with technology that doesn’t work as it should and become disillusioned and disengaged.

Instilling AI confidence in employees

Blind investment and faith in AI is not a comprehensive strategy, and no matter how advanced the tools, the workforce needs to know how to use them for maximum impact. Continuous training and education, therefore, need to be part of the package for AI tools to succeed.

When employees feel confident in their ability to use AI tools and applications, they will be more confident in experimenting with what AI-savviness means to them in their role and share discoveries and new use cases with their team, feeding into the culture of collaboration and creativity that defines an AI-savvy business.

Reaching AI savviness

At its core, being AI-savvy means avoiding the lure of the next big technology and instead nurturing a culture that emphasizes AI education, thoughtful procurement and decisive implementation practices. It also means remaining dynamic and adapting to changes as the AI wave continues to crest. Prioritizing proactive strategic thinking, strong data foundations and an open environment that fosters creativity and innovation creates a fertile environment for exciting and effective AI use as the technology develops.

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